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A Novel Alignment Method for SINS with Large Misalignment Angles Based on EKF2 and AFIS.

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This study introduces a new filtering alignment method for strapdown inertial navigation systems (SINS) using the second-order extended Kalman filter (EKF2) and adaptive fuzzy inference system (AFIS) for improved accuracy with large initial misalignment angles.

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Area of Science:

  • Navigation Systems Engineering
  • Control Theory
  • Signal Processing

Background:

  • Strapdown inertial navigation systems (SINS) require precise initial alignment, which is challenging under large initial misalignment angles.
  • Traditional alignment methods struggle with convergence and accuracy when initial attitude errors are significant.

Purpose of the Study:

  • To propose a novel filtering alignment method for SINS that effectively handles large initial misalignment angles.
  • To enhance the accuracy and convergence speed of SINS alignment using advanced filtering techniques.

Main Methods:

  • A second-order nonlinear state equation is formulated using quaternion representation for attitude errors and incorporating nonlinear velocity and attitude error models.
  • A linear measurement equation is derived from SINS velocity outputs.
  • A filtering scheme is designed by integrating the second-order extended Kalman filter (EKF2) with an adaptive fuzzy inference system (AFIS) to adjust the covariance matrix Pk/k-1.

Main Results:

  • Monte Carlo simulations demonstrate rapid convergence of pitch, roll, and yaw misalignment errors to approximately 14″, 15″, and 7.62' within 350 seconds, even with initial errors ranging from -40° to 40° (pitch/roll) and -50° to 50° (yaw).
  • The method shows robust convergence even with extremely large initial misalignment angles (e.g., 80°, 120°, 170°), attributed to the AFIS's adaptive covariance matrix adjustment.
  • Turntable experiments validated the proposed method's effectiveness and superiority over other nonlinear alignment techniques.

Conclusions:

  • The proposed EKF2 and AFIS-based filtering alignment method offers a robust and accurate solution for SINS alignment under significant initial misalignment conditions.
  • The adaptive capability of the fuzzy inference system is crucial for maintaining convergence and accuracy in challenging alignment scenarios.
  • This advanced alignment technique holds significant promise for improving the performance of navigation systems in various applications.